Abstract
This paper presents a SLAM solution based on Rao-Blackwellized particle filters supported by a fast scan matching algorithm for pose refinement and a flexible space management data structure. By taking advantage of independence between particles its computational efficiency is further improved through multithreading. We have evaluated the efficiency of the solution by using several publicly available datasets and compared the results with the popular solution GMapping. The obtained results show the proposed approach provides a fast and accurate particle filter SLAM suitable for real-time operations.
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